Kernel Principal Component Analysis (KPCA)
Non-linear dimensionality reduction through the use of kernels
| Configuration: |
n_components
Number of components. If None, all non-zero components are kept.
kernel
Kernel. Default=”linear”.
degree
Degree for poly kernels. Ignored by other kernels.
gamma
Kernel coefficient for rbf, poly and sigmoid kernels. Ignored by other
kernels.
coef0
Independent term in poly and sigmoid kernels.
Ignored by other kernels.
alpha
Hyperparameter of the ridge regression that learns the
inverse transform (when fit_inverse_transform=True).
fit_inverse_transform
Learn the inverse transform for non-precomputed kernels.
(i.e. learn to find the pre-image of a point)
remove_zero_eig
If True, then all components with zero eigenvalues are removed, so
that the number of components in the output may be < n_components
(and sometimes even zero due to numerical instability).
When n_components is None, this parameter is ignored and components
with zero eigenvalues are removed regardless.
eigen_solver
Select eigensolver to use. If n_components is much less than
the number of training samples, arpack may be more efficient
than the dense eigensolver.
tol
Convergence tolerance for arpack.
If 0, optimal value will be chosen by arpack.
max_iter
Maximum number of iterations for arpack.
If None, optimal value will be chosen by arpack.
random_state
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by np.random. Used when eigen_solver == ‘arpack’.
n_jobs
The number of parallel jobs to run.
None means 1 unless in a joblib.parallel_backend context.
-1 means using all processors. See n_jobs
for more details.
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| Attributes: |
lambdas_
Eigenvalues of the centered kernel matrix in decreasing order.
If n_components and remove_zero_eig are not set,
then all values are stored.
alphas_
Eigenvectors of the centered kernel matrix. If n_components and
remove_zero_eig are not set, then all components are stored.
dual_coef_
Inverse transform matrix. Only available when
fit_inverse_transform is True.
X_transformed_fit_
Projection of the fitted data on the kernel principal components.
Only available when fit_inverse_transform is True.
X_fit_
The data used to fit the model. If copy_X=False, then X_fit_ is
a reference. This attribute is used for the calls to transform.
|
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| Inputs: | |
|---|
| Outputs: |
- model : model
Model
|
|---|
- Output ports:
-
- Configuration:
- n_components
- Number of components. If None, all non-zero components are kept.
- kernel
- Kernel. Default=”linear”.
- degree
- Degree for poly kernels. Ignored by other kernels.
- gamma
- Kernel coefficient for rbf, poly and sigmoid kernels. Ignored by other
kernels.
- coef0
- Independent term in poly and sigmoid kernels.
Ignored by other kernels.
- alpha
- Hyperparameter of the ridge regression that learns the
inverse transform (when fit_inverse_transform=True).
- fit_inverse_transform
- Learn the inverse transform for non-precomputed kernels.
(i.e. learn to find the pre-image of a point)
- remove_zero_eig
- If True, then all components with zero eigenvalues are removed, so
that the number of components in the output may be < n_components
(and sometimes even zero due to numerical instability).
When n_components is None, this parameter is ignored and components
with zero eigenvalues are removed regardless.
- eigen_solver
- Select eigensolver to use. If n_components is much less than
the number of training samples, arpack may be more efficient
than the dense eigensolver.
- tol
- Convergence tolerance for arpack.
If 0, optimal value will be chosen by arpack.
- max_iter
- Maximum number of iterations for arpack.
If None, optimal value will be chosen by arpack.
- random_state
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by np.random. Used when eigen_solver == ‘arpack’.
- n_jobs
The number of parallel jobs to run.
None means 1 unless in a joblib.parallel_backend context.
-1 means using all processors. See n_jobs
for more details.
Some of the docstrings for this module have been automatically
extracted from the scikit-learn library
and are covered by their respective licenses.
-
class
node_decomposition.KernelPCA[source]
